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Hazrat Ali is a Lecturer in Artificial Intelligence and Data Science. He is an experienced educator and researcher with over a decade of experience in the field. He has honed his skills at some of the most prestigious universities and research institutes across the globe, including working as a researcher at the Biomedical Engineering R&D centre of Umea University Sweden and Hamad Bin Khalifa University Qatar. His extensive research portfolio spans various areas of Generative AI, Medical AI, healthcare, and computer vision, with a specific focus on generative adversarial networks for medical imaging, deep learning for ultrasound medical imaging, and AI for healthcare. He has developed AI methods for medical images for applications such as GANs for disease transformation, GANs for medical imaging super-resolutions, and deep learning for motor unit segmentation, which have been widely adopted and cited by the research community. He made contributions to deep learning for ultrasound imaging and the VIPVIZA projects at Umea University Sweden. In addition, he has developed open-source datasets, including a dataset of natural scene images with Urdu text, the (the first dataset on Urdu handwritten characters), and a . These datasets, available on the Kaggle platform, have proven to be invaluable resources for AI-oriented tasks and have contributed to advancing the field of AI.

He has authored more than 60 peer-reviewed articles published in top-tier international journals and conferences including IEEE Transactions on Image Processing, Nature Scientific Reports, IEEE CVPR workshop, and NeurIPS workshops. He is the lead editor of the book published by Springer Nature. He is a senior member of IEEE, and a member of the Swedish AI Society. He serves as Associate Editor at IEEE and as Academic Editor at PeerJ Computer Science. He is lead guest editor for special issue on . He has organized and/or chaired several workshops/conferences including the first workshop on Foundation AI Models in Biomedical Imaging at the 2025 IEEE International Conference on Biomedical Imaging, a special session on AI in Surgery at the UK premier conference on Medical Imaging Understanding and Analysis (MIUA 2025), and a special session on Multimodal Generative AI in Healthcare at the AI in Healthcare conference (AIiH 2025). He has served as session chair at Machine Intelligence and Signal Processing (MISP 2022). He has been the lead organizer of the 2021 NeurIPS meet-up in Doha, Qatar and the 2020 meet-up in Umea, Sweden. He is PC member at leading international conferences including IEEE International Conference on Artificial Intelligence ICAI 2024, IEEE CVPR Workshop on WiCV2024, and MICCAI. He has delivered invited talks at various national and international venues including St. John University New York USA, Biomedical Engineering R & D of Umea University Sweden, and International Conference on Computational Sciences and Technology (INCCST 2019). He was selected as a young researcher at the 5th Heidelberg Laureate Forum, Heidelberg, Germany. He is the recipient of the DAAD AI Networking Fellowship Germany 2021, the Best Researcher Award by COMSATS University (2020-2021), and the Kempe Foundation Sweden grant for postdoc research, the HEC PhD Scholarship, the scholarship grant by European Machine Learning school, the Top 10 research pitch award by the University of Queensland, Australia, IEEE Student Travel Award, the IBRO grant, the USTB excellent international student award, and the Erasmus Mundus STRoNGTiES research grant.

Professional membership:

• Senior Member, IEEE
• Associate Fellow, Advance-HE

Highlights

• Associate Editor, IEEE Access
• Associate Editor, PeerJ Computer Science
• Lead Guest Editor,
• Lead organizer and chair, Special Session on at the 2025 AI in Healthcare conference, in Cambridge, UK.
• Lead organizer and chair, Special Session on at the 2025 Medical Imaging Understanding and Analysis conference, MIUA 2025.
• Workshop Chair, Foundation AI Models in Biomedical Imaging at

Generative AI, Medical AI, Deep Learning, Healthcare

Machine Learning

Data Strategy

Relational and non-relational databases

Previously taught: Digital Signal Processing, Signals & Systems, Python programming